Broadband services such as broadcast TV, video on demand (VoD), telecommunication systems, or the Internet have played an increasingly important role. As a result, in order to obtain a lower cost-per-bit effective transport of signals, in particular data to be transported, a new generation of data transmission network technology has been developed.
New generation data transmission networks demand a high degree of synchronization between network transmission elements. Here, an important factor for all network transmission elements is the timing of the network transmission elements. In particular, phase variations in reference clock frequencies governing synchronous network elements may introduce errors at various stages in the network.
One measure of timing errors in synchronous data transmission networks is known as the maximum time interval error (MTIE) and is derived from a plurality of time interval error values. A time interval error (TIE) value is defined as the difference between an actual clock and the reference clock, and for any given sampling period, the maximum time interval error is defined as the maximum peak-to-peak difference of time interval error values within this sampling period.
US 2009/0161744 A1 discloses a method for estimating a maximum time interval error in a data transmission network, using information derived from timing pseudowire or packed data flows.
However, implementing directly the definition of the maximum time interval error does not permit real-time estimation and, therefore, a real-time display of the results. In particular, the maximum time interval error is generally required to be estimated in parallel for a set of different sampling periods, to reveal information about the time varying behavior of a signal, and aid in the diagnosis of faults. These sampling periods typically range from one second up to a day or more. Obtaining the results for such periods conventionally requires a large quantity of data to be collected and, thus, even for the shortest sampling period, the maximum time interval error cannot be calculated until the entire dataset has been gathered.
Further, there is a large amount of storage capacity and computing time required to obtain the estimated values of the maximum time interval error, particularly for long sampling periods.